title: [Chronic obstructive pulmonary disease prevalence estimated using a standard algorithm based on electronic health data in various areas of Italy].
abstract: OBJECTIVE
  to estimate the prevalence of chronic obstructive pulmonary disease (COPD) by integrating various administrative health information systems.
  
  
  METHODS
  prevalent COPD cases were defined as those reported in the hospital discharge registry (HDR) and cause of mortality registry (CMR) with codes 490*, 491*, 492*, 494* and 496* of the International diseases classification 9th revision. Annual prevalence was estimated in 35+ year-old residents in six Italian areas ofb different sizes, in the period 2002-2004. We included cases observed in the previous four years who were alive at the beginning of each year.
  
  
  RESULTS
  in 2003, age-standardized prevalence rates varied from 1.6% in Venice to 5% in Taranto. Prevalence was higher in males and increased with age. The highest rates were observed in central (Rome) and southern (Taranto) cities, especially in the 35-64 age group. HDR contributed 91% of cases. Health-tax exemption registry would increase the prevalence estimate by 0.2% if used as a third data source.
  
  
  CONCLUSIONS
  with respect to the National Health Status survey, COPD prevalence is underestimated by 1%-3%; this can partly be due to the selection of severe and exacerbated COPD by the algorithm used. However, age, gender and geographical characteristics of prevalent cases were comparable to national estimates. Including cases observed in previous years (longitudinal estimates) increased the point estimate (yearly) of prevalence two or three times in each area.
authors.affiliation: Dipartimento di epidemiologia, ASL RME, Roma. faustini@asplazio.it
authors.forename: Annunziata
authors.initials: A
authors.lastname: Faustini
authors.forename: Silvia
authors.initials: S
authors.lastname: Cascini
authors.forename: Massimo
authors.initials: M
authors.lastname: Arcà
authors.forename: Daniela
authors.initials: D
authors.lastname: Balzi
authors.forename: Alessandro
authors.initials: A
authors.lastname: Barchielli
authors.forename: Cristina
authors.initials: C
authors.lastname: Canova
authors.forename: Claudia
authors.initials: C
authors.lastname: Galassi
authors.forename: Enrica
authors.initials: E
authors.lastname: Migliore
authors.forename: Sante
authors.initials: S
authors.lastname: Minerba
authors.forename: Maria Angela
authors.initials: MA
authors.lastname: Protti
authors.forename: Anna
authors.initials: A
authors.lastname: Romanelli
authors.forename: Roberta
authors.initials: R
authors.lastname: Tessari
authors.forename: Maria Angela
authors.initials: MA
authors.lastname: Vigotti
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Simonato
mesh_terms: D000328:Adult; D000465:Algorithms; D002391:Catchment Area, Health; D002648:Child; D002675:Child, Preschool; D001330:Electronic Data Processing; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007558:Italy / Q000453:epidemiology; D008297:Male; D029424:Pulmonary Disease, Chronic Obstructive / Q000453:epidemiology*; D055815:Young Adult